Data Science Applications

Introduction

Now that we know a bit about how to do data science, what exactly can we do with it?

In this article, we'll look at how we can apply data science thinking to different problems. We'll explore several common applications, including:

Reports

Recommender Systems

Dynamic Pricing

Natural Language Processing

Similar to how data science is made up of different disciplines, the applications of data science are far ranging. But in all of these situations, you will apply data science to find patterns, draw meaningful conclusions, and make decisions.

Don't worry if these terms sound intimidating! Throughout the article, we'll break down each of them so that you have a solid understanding of what you can build with data science.

Reports

Reports are the most fundamental application of data science. A report is a document in which you present your process and your findings. Reports are important because they enable those who work with data to translate numbers and calculations into accessible insights and recommendations for team members.

A report could take the form of a publication that circulated within your company, or an article that's published on the internet, or as part of a conference presentation.

A good report should have the following characteristics:

Simple: non-technical team members will need to be able to quickly grasp findings

Clear: language should be to the point

Engaging Presentation: charts and presentations should be well designed

Five-Thirty-Eight is a popular statistics website that publishes reports on a range of topics, such as politics and sports. Take a look at this piece that they did on the Bechdel Test, along with a range of other new tests that examine the presence of gender and race in film.

Recommendation Engines

One of the more well-known applications of data science is using data and machine learning to build a recommender system, also known as a recommendation engine.

A recommender system is a type of content filtration system that seeks to predict what a user would be interested in consuming. These suggestions could come from the user's preferences that they've shared with the platform, like how Amazon suggests things you might want to buy based off of previous purchases. Other recommender systems work by looking at the preferences of people in your network, or those of people with similar demographics.

Recommender systems work for all types of information, from Spotify using it to recommend new artists to Netflix predicting what will be your next binge fest.

Dynamic Pricing

Another data science application is dynamic pricing. Ever go to buy an airplane ticket but then 5 minutes later the pricing changes? You have dynamic pricing to thank for that.

Dynamic pricing, also known as surge pricing, is the practice of setting prices for products or services based on market demand. Companies that use dynamic pricing build algorithms that take into account competition, supply and demand, as well as other factors related to the specifics of the industry. Dynamic pricing exists across several industries, including transportation, entertainment, amusement parks, and professional sports.

The most common example is airline tickets. Airlines started to use computers to determine flight prices as early as the 1950s, taking into account the season, day of the week, and time of day when setting ticket prices. However, airlines have recently come under scrutiny for utilizing more robust dynamic pricing techniques. Several are now determining fares based on the buyer.

While dynamic pricing is increasingly common, its a tactic that is often seen to benefit the company more than the consumer. In a recent example, the app MoviePass decided to implement surge pricing as part of their ticket buying experience. Previously, customers were able to pay a flat monthly rate and see an unlimited number of movies. However, the company recently implemented a change where tickets for certain movies or theater that are more popular will incur an additional fee.

Natural Language Processing

While data science typically makes you think of numbers, it can also be helpful in recognizing trends and patterns in language.

Natural Language Processing (often referred to as NLP) is the application of programming and artificial intelligence to process and analyze text. It can be used for research purposes, to understand, for example, the grammatical structure of a text, to more creative pursuits such as generative poetry.

In NLP, your datasets are made up of examples of language usage, known as a corpus. After training on this dataset, a machine can then perform different functions, such as part-of-speech tagging, language translation, and sentiment analysis.

A common application of NLP is chatbots. While many chatbots follow pre-determined scripts, more advanced ones can use NLP to enable a dynamic discourse. NLP enables a bot to continue learning as it talks, making it better at handling different and unexpected situations.

Summary

As you can see, there are many different applications and kinds of projects that you can do once you know a bit of data science!

Reports - a way of presenting your process, insights, and recommendations

Recommender Systems - a process that uses data about users and items to predict interest

Dynamic Pricing - a strategy that takes into account factors such as demand to increase and decrease prices to drive profit

Natural Language Processing - ways of analyzing text to gain insights as well as support applications, such as chatbots

Of course, there are many applications beyond just the ones that we've covered here. So no matter your interest or professional industry, data science thinking can create impact.